Core idea of this paper:
To evaluate prompt engineering techniques for enhancing AI understanding and accuracy
To assess the impact of CoT reasoning and entity relationship extraction on AI performance
To analyze AI models based on accuracy, instruction adherence, and stop-word handling
The challenge
As AI models continue to evolve, their ability to understand accurately and generate responses remains a fundamental challenge. Despite advancements in NLP and LLMs, these systems often struggle with context retention, ambiguous phrasing, multi-step reasoning, and instruction adherence. Ensuring that AI-generated responses are both precise and contextually appropriate requires more than just advanced model architectures. It necessitates strategic interaction techniques that guide the AI towards the desired output.
Enhancing Response Accuracy through COT Entity Relationship Extraction
One of the most effective techniques for improving AI comprehension is prompt engineering, which involves crafting well-structured prompts to enhance the model’s reasoning and response quality. Prompt engineering enables AI models to break down multi-step reasoning tasks, reducing errors in logic and improving response coherence. The significance of prompt engineering extends beyond simple text generation—it plays a crucial role in applications such as recommendation systems, summarization, customer support chatbots, and knowledge retrieval. By leveraging structured techniques like Chain of Thought (CoT) reasoning and entity relationship extraction, prompt engineering helps AI models improve their interpretative abilities, ensuring that responses are not only relevant but also logically sound.
Summary of Key Findings
The benchmarking study assessed AI model performance across accuracy, instruction adherence, and stop-word handling, with evaluations spanning models from Gemma2-9b-It to GPT-4-Turbo. Chain of Thought (CoT) reasoning with Named Entity Recognition (NER) extraction proved superior due to its enhanced accuracy, interpretability, domain adaptability, and reduced error propagation. Among the tested models, Gemini-2.0-Flash emerged as the best overall with the highest accuracy (85.19%), while Llama3-70B-8192 was the fastest (1.6s). Qwen-2.5-32B offered a strong balance between speed and performance. The study underscores the value of structured reasoning techniques in improving AI comprehension and response accuracy.
